OL-JCMSR: A Joint Coding Monitoring Strategy Recommendation Model Based on Operation Log

Guoqiang Sun, Peng Xu, Man Guo, Hao Sun, Zhaochen Du, Yujun Li and Bin Zhou
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Guoqiang Sun: School of Information Science and Engineering, Shandong University, Qingdao 266237, China
Peng Xu: School of Information Science and Engineering, Shandong University, Qingdao 266237, China
Man Guo: Ministry of Science and Technology, Gwacheon 13809, Korea
Hao Sun: School of Information Science and Engineering, Shandong University, Qingdao 266237, China
Zhaochen Du: Hisense State Key Laboratory of Digital Multimedia Technology, Qingdao 266061, China
Yujun Li: School of Information Science and Engineering, Shandong University, Qingdao 266237, China
Bin Zhou: School of Information Science and Engineering, Shandong University, Qingdao 266237, China

Mathematics, 2022, vol. 10, issue 13, 1-14

Abstract: A surveillance system with more than hundreds of cameras and much fewer monitors strongly relies on manual scheduling and inspections from monitoring personnel. A monitoring method which improves the surveillance performance by analyzing and learning from a large amount of manual operation logs is proposed in this paper. Compared to fixed rules or existing computer-vision methods, the proposed method can more effectively learn from the operators’ behaviors and incorporate their intentions into the monitoring strategy. To the best of our knowledge, this method is the first to apply a monitoring-strategy recommendation model containing a global encoder and a local encoder in monitoring systems. The local encoder can adaptively select important items in the operating sequence to capture the main purpose of the operator, while the global encoder is used to summarize the behavior of the entire sequence. Two experiments are conducted on two data sets. Compared with att-RNN and att-GRU, the joint coding model in experiment 1 improves the Recall@20 by 9.4% and 4.6%, respectively, and improves the MRR@20 by 5.49% and 3.86%, respectively. In experiment 2, compared with att-RNN and att-GRU, the joint coding model improves by 11.8% and 6.2% on Recall@20, and improves by 7.02% and 5.16% on MRR@20, respectively. The results illustrate the effectiveness of the our model in monitoring systems.

Keywords: monitoring strategy; joint coding model; operation log; monitoring system; recommendation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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